ReviewACS omega2022
A Generative Approach to Materials Discovery, Design, and Optimization.
Review in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Data-Driven Approach for Bioreactor Monitoring and Prediction in Monoclonal Antibody Production.ACS omega · 2026Article
- Review
- From Experiment-Driven to Theory- and Data-Driven: A Computational Paradigm Shift in High-Entropy Electrocatalyst Design.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Application of Machine Learning in Predicting the Properties of Two-Dimensional Semiconductor Materials.Nanomaterials (Basel, Switzerland) · 2026Review
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Architecting Functional Polymers: Advances in Modular Synthesis, Responsive Design, and Multifaceted Applications.Polymers · 2026Review
- AI-driven peptide discovery for endometrial cancer: deep generative modeling and molecular simulation in the big data era.Journal of computer-aided molecular design · 2026Article
- Two-dimensional layered metal oxides (2D LMOs) for next-generation electronic devices.Nanoscale advances · 2026Review
- Applications and Advances of Machine Learning in the Development of Solid-State Electrolytes for Lithium-Ion Batteries.ACS omega · 2025Review
- Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing.Gels (Basel, Switzerland) · 2025Review
- EcoCurrentNet an integrated DNN-CatBoost model for predicting optoelectronic material performance under varying conditions.Scientific reports · 2025Article
- Review
- 33 Unresolved Questions in Nanoscience and Nanotechnology.ACS nano · 2025Article
- Transition metal coordination polymer-derived materials for supercapacitor applications: recent advances and future perspectives.Royal Society open science · 2025Review
- The development of the generative adversarial supporting vector machine for molecular property generation.Journal of cheminformatics · 2025Article
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- Unveiling the Structural Stability and Optoelectronic Properties of Pristine and Janus Monolayers.ACS omega · 2025Article
- Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation.Environmental science & technology · 2025Review
- AI-Based Drug Discovery and Design for Different Genetic Designs.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Toward an AI Era: Advances in Electronic Skins.Chemical reviews · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite its potential to transform society, materials research suffers from a major drawback: its long research timeline. Recently, machine-learning techniques have emerged as a viable solution to this drawback and have shown accuracies comparable to other computational techniques like density functional theory (DFT) at a fraction of the computational time. One particular class of machine-learning models, known as "generative models", is of particular interest owing to its ability to approximate high-dimensional probability distribution functions, which in turn can be used to generate novel data such as molecular structures by sampling these approximated probability distribution functions. This review article aims to provide an in-depth understanding of the underlying mathematical principles of popular generative models such as recurrent neural networks, variational autoencoders, and generative adversarial networks and discuss their state-of-the-art applications in the domains of biomaterials and organic drug-like materials, energy materials, and structural materials. Here, we discuss a broad range of applications of these models spanning from the discovery of drugs that treat cancer to finding the first room-temperature superconductor and from the discovery and optimization of battery and photovoltaic materials to the optimization of high-entropy alloys. We conclude by presenting a brief outlook of the major challenges that lie ahead for the mainstream usage of these models for materials research.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.